Within-Subject Variability and Boosting of T-Cell Interferon-γ Responses after Tuberculin Skin Testing
Bibliographic record
Abstract
RATIONALE: The optimal strategy for the diagnosis of latent tuberculosis infection is controversial. Adoption of a two-step strategy (tuberculin skin test [TST] followed by an IFN-gamma release assay [IGRA], compared with an IGRA alone), may be limited by TST-mediated boosting of subsequent IGRA responses. Assessment of within-subject IGRA variability will aid in establishing thresholds for conversions and reversions, and interpretation of serial testing results. OBJECTIVES: To determine short-term IGRA variability and the impact of TST on subsequent IGRA results. METHODS: Within-subject variability and TST-mediated boosting of IGRA responses were evaluated in 26 South African participants with varying exposure risk. IGRAs (T-SPOT.TB, QuantiFERON-TB Gold In-Tube [QuantiFERON-TB-GIT], PPD, and heparin-binding hemagglutinin) were repeated four times over 21 days pre-TST, and on Days 3, 7, 28, and 84 post-TST administration. MEASUREMENTS AND MAIN RESULTS: All participants showed within-subject IGRA variability. Changes of +/-3 spots (T-SPOT.TB) or +/-80% from the mean IFN-gamma response (QuantiFERON-TB-GIT) over 3 weeks explained 95% of the variability. Spontaneous conversions/reversions occurred in 7 of 26 subjects (27%) (6 for T-SPOT.TB and 1 for QuantiFERON-TB-GIT [P = 0.049]) during the within-patient variability studies (pre-TST). After the TST eight subjects (33%) boosted above the defined baseline variability. By Day 7 post-TST, but not Day 3, 2 (12.5%) initially IGRA-negative test subjects converted. By contrast, boosting of PPD and heparin-binding hemagglutinin occurred by Day 3 post-TST. CONCLUSIONS: When using a two-step screening strategy it appears safe to perform a QuantiFERON-TB-GIT or T-SPOT.TB IGRA within 3 days of performing the TST. A 3-spot or 80% IFN-gamma response variation, on either side of baseline values, explains 95% of the short-term variability and may be useful for interpreting conversions and reversions, and values close to the cut-point.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".